NumPy Cheat Sheet for Data Scientists
40 Methods used 95% of the time.
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NumPy Cheat Sheet for Data Scientists
Here’s a NumPy cheat sheet that depicts the 40 most commonly used methods from NumPy:
In our experience, you will use these methods 95% of the time working with NumPy.
It is important to understand that whenever you are learning a new library, mastering/practicing each and every method is not necessary.
Instead, put Pareto’s principle to work—20% of your inputs contribute towards generating 80% of your outputs.
👉 Over to you: Have I missed any commonly used methods?
Thanks for reading!
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
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Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn how to build Agentic systems in an ongoing crash course with 6 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data in this crash course.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.